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Early cancer detection using computer-aided diagnosis (CAD) models improves patient survival. This review examines CAD approaches, including deep learning, for diagnosing various tumors from medical images.

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Area of Science:

  • Medical Imaging Analysis
  • Computational Pathology
  • Oncology

Background:

  • Tumor detection is crucial for survival, but imaging challenges like low contrast and poor borders hinder early diagnosis.
  • Computer-aided diagnosis (CAD) models offer a promising solution for accurate tumor identification.

Purpose of the Study:

  • To review existing computer-aided diagnosis (CAD) approaches for detecting tumors in breast, brain, lung, liver, skin, and colon cancers.
  • To focus on decision-making systems utilizing handcrafted features and deep learning architectures.

Main Methods:

  • Review of current literature on CAD systems for tumor detection across multiple cancer types.
  • Analysis of diagnostic modalities including CT, MRI, colonoscopy, mammography, dermoscopy, and histopathology.
  • Examination of both traditional handcrafted feature extraction and modern deep learning techniques.

Main Results:

  • CAD models, particularly those employing deep learning, show significant potential in overcoming imaging limitations for tumor detection.
  • A variety of approaches exist, leveraging diverse imaging modalities and feature extraction methods.
  • The review synthesizes current strategies for tumor diagnosis in key cancer types.

Conclusions:

  • Computer-aided diagnosis is vital for enhancing the accuracy and efficiency of tumor detection.
  • Deep learning architectures represent a significant advancement in CAD for oncology.
  • Further research into robust CAD systems can improve early cancer diagnosis and patient outcomes.